Abstract: With the large-scale development of Electric Vehicle (EV), their regulatory potential as "mobile energy storage units" cannot be overlooked, which profoundly influence the operational paradigm of power systems. In the context of EV grid integration, fully considering the dual characteristics of EV as controllable loads and mobile energy storage, a comprehensive dynamic charging/discharging scheduling model for EV is constructed, incorporating multiple key factors such as EV charging demand, dynamic electricity prices, time-coupling constraints of energy storage, and battery degradation. To address the randomness of EV charging start times and initial states, as well as the curse of dimensionality and convergence difficulties of traditional reinforcement learning methods in scenarios with continuous decision variables, an intelligent charging/discharging control and optimal scheduling algorithm based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. Through continuous interaction between the agent and the environment and the design of a reward feedback mechanism, this algorithm can make optimal charging/discharging decisions based on electricity price fluctuations, ensuring that the expected charging capacity is achieved after the charging process, thereby realizing intelligent control and optimal scheduling of EV charging/discharging behavior to minimize charging costs. Simulations based on real-world scenario data demonstrate that the proposed algorithm effectively adapts to dynamic electricity price changes in smart grids and significantly reduces charging costs for EV users. Compared with a series of mainstream algorithms (such as DDPG, DQN, PSO, etc.), the proposed algorithm reduces charging costs by 4.41% to 24.23%, fully validating its performance and economic advantages.

Key words: smart grid, electric vehicle, vehicle-to-grid, charge/discharge scheduling, deep reinforcement learning